At places such as entries of stores, museums, exhibition halls, gymnasiums etc., entries of long passages of escalators and corridors in the buildings, and doors of passenger vehicles for getting on or off, the amount of the people entering and leaving at a certain time point or in a time period is required to be counted, and the services can be optimized and the management level can be improved based on the statistic data. The statistic method for counting the number of the people based on the photoelectric technology cannot be accepted by users because the statistic precision is low, and the image cannot be stored for manually counting afterwards.
Recently, some researches on counting the number of people based on image recognition technology has been developed. In these systems, it is assumed normally that the background in the working scene of the camera is basically constant, the illumination is constant during a long period of time, the distribution of the illumination is basically equilibrium spatially, whether the people is present or not does not effect the illumination for the operation of the camera, the people present actively some single features, and the gesture of the human body in the visual field of the camera basically does not change significantly.
For example, an image statistic system for counting the client flow rate in a store has been publicized in a master's degree paper “A Multi-target Counting Method Based on Dynamic Images” written by Fu Xiao Wei of Institute of Information Science and Engineering of Wu Han Science and Technology University. As shown in the flow chart of the system in FIG. 1, it comprises the steps of: acquiring dynamic image 11, detecting the movement 12, classifying the targets and extracting the features 13, tracking multiple targets 15, counting the targets 15, etc. In the system, the moving target region is segmented by using the methods, such as the difference between frames, the difference between backgrounds, and adaptive threshold, and the head area of a human body is extracted by using image filtering, image segmenting, morphologic processing and features statistic, and the tracking and counting of multiple targets are performed by using a feature tracking method. The subject studied by the author of the paper is counting the client flow rate of the store, however, the author of the paper only simulated in the laboratory a simple condition that the people pass by. There is significant difference between the simple simulated condition in the laboratory and the complicated condition at the entries and exits of the store where the people goes in and out, and the multi-target counting method publicized in the paper can only accomplish the multi-target recognition and counting of the multiple targets in a simulated site in the laboratory, while it is unable to manage the statistic task of the client flow rate in a store under a practical application condition. Furthermore, the problems, such as that the distribution of the illumination is extremely not equilibrium spatially, the change in a time period is complicated, the change of the gesture during the people goes by is evident, and the like, have not been considered in counting the people flow rate at the entries and exits. Under the conditions that the distribution of the illumination is extremely not equilibrium spatially, the change in a time period is complicated, not only large difference exists between the target and the background, but also large difference exists among the backgrounds exists. It is difficult to detect the moving area under such complicated condition by using the method publicized in the paper. In many applications similar to the situation that the passengers get on or off a vehicle, the change of the gesture is significant during the people passing by, the head of a human body may often be sheltered, sometimes. It cannot be distinguished whether a person is in a certain area or not by extracting simply the feature of the head of a person. Because the illumination changes significantly, the change of the feature is evident, and it is difficult to track correctly multiple-targets by using a single feature tracking method.
In a paper “A Study on Passenger Flow Rate Counting System Based on Video” is publicized on the National Conference of Image and Graphic Technology, 2005, by Tian Jing Lei of Signal and Image Processing Institute of Da Lian Maritime University (“A Collection of the Papers of the 12th National Conference of Image and Graphic Technology”, the author: Zhang Yu Jin, ISBN: 7302119309), a passenger flow rate counting system based on video is discussed in the paper, as shown in the flow chart of the system in FIG. 2. It comprises the steps of: acquiring the video 21, motion segmenting 22, recognizing the target 23, tracking the target 24, counting 25, etc. In this paper, a method of image frame difference is used to complete motion segmenting 22; recognizing the target 23 is performed by a Fuzzy C Mean (FCM) using Cluster method upon the parameters, such as the area, length, and width of the motion area, and by using a cluster method of fuzzy C—the mean (FCM); tracking the target 24 is performed by using a centroid based method; human body counting 25 is performed by deciding whether the moving target traverses two preset counting lines or not based on the positions of the center points of two successive frames. Similar to that publicized in the above master's degree paper, the present method for extracting the moving target publicized in the paper is similar as that publicized in the above master's degree paper, both uses a simple method of frame difference. The only difference is that difference between three frames is utilized substituting the difference between two frames. Therefore, under the condition that the distribution of the illumination is extremely not equilibrium spatially, the change is complicated in a time period, the detection of the human body area cannot be performed correctly. Only a simple condition of a indoor room passage is discussed for the system, however, the complicated problems existing at the entries and exits under normal application conditions, such as that the distribution of the illumination in the operation working region of the camera traversing from inside the room to the outside is extremely not equilibrium spatially, the change in a time period is complicated, the change of the gesture during the people goes by is evident, and the like, have not been considered.
Similarly, the operation principles, the basic assumptions and the implementation methods of the other publicized systems for counting the number of the passengers based on image recognition are similar to the methods publicized in the above two papers. The problems of counting of the number of the passengers at entries and exits under normal application conditions, such as that the distribution of the illumination is extremely not equilibrium spatially, the change in a time period is complicated, the change of the gesture during the people go by is evident, and the like, cannot been solved.